Dear colleagues,
The INFORMS Computing Society is pleased to announce the recipients of the 2025 ICS Harvey J. Greenberg Research Award, which honors research excellence in the field of computation and operations research applications, especially those in emerging application fields.
Winners:
Braden L. Crimmins, J. Alex Halderman, and Bradley Sturt
Improving the Security of United States Elections with Robust Optimization
Citation: This paper introduces the first formal approach to designing test decks for logic and accuracy testing (LAT), a procedure that election officials have used for over a century to verify the correctness of voting machines. By employing robust optimization, the authors develop test decks that guarantee detection of any machine misconfiguration that could swap votes across candidates, while minimizing the number of ballots required. Their cutting-plane–based algorithm efficiently solves these optimization problems at scale, and a retrospective study of Michigan's November 2022 general election shows that their method achieves rigorous security guarantees with only 1.2% more ballots than current practice. The approach has since been piloted by the Michigan Bureau of Elections, offering a low-cost and practical solution to strengthen election security and public trust in democratic institutions.
The committee believes this work embodies the spirit of Harvey J. Greenberg's legacy by combining rigorous mathematical techniques (specifically, robust optimization) with a timely and socially important challenge in election security. The paper also demonstrates real-world impact through its pilot program with the Michigan Bureau of Elections.
In addition, the committee recognizes two other submissions with an honorable mention.
Honorable Mention 1:
Soroush Saghafian
Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach
Citation: This paper introduces a novel framework for designing treatment guidelines when traditional assumptions in causal inference do not hold. Standard Dynamic Treatment Regimes (DTRs) often fail in real-world applications such as medicine and public policy, particularly when unobserved confounders exist and evolve over time. To address this challenge, the authors propose Ambiguous Dynamic Treatment Regimes (ADTRs), which evaluate treatment policies against a "cloud" of plausible causal models. By connecting ADTRs to Ambiguous Partially Observable Markov Decision Processes (APOMDPs), the paper develops reinforcement learning methods that efficiently learn effective treatment regimes from observational data. Theoretical guarantees, including consistency and asymptotic normality, are established, and the approach is validated both through simulations and a hospital case study on patients who developed New Onset Diabetes After Transplantation (NODAT).
Honorable Mention 2:
Xiao-Yue Gong and David Simchi-Levi
Bandits atop Reinforcement Learning: Tackling Online Inventory Models with Cyclic Demands
Citation: addresses the long-standing gap between inventory theory and practice by studying online inventory models under unknown cyclic demand distributions. The authors design reinforcement learning algorithms that leverage structural properties of inventory problems to achieve near-optimal regret bounds, surpassing existing theoretical results. They analyze both lost-sales and multi-product backlogging models, introducing episodic formulations and extending them to non-discarding models through a novel bandit-based approach, Meta-HQL. Their algorithms match the regret lower bounds they establish, while removing dependence on the size of the state–action space. Empirical studies with real sales data from Rossmann and synthetic benchmarks demonstrate rapid convergence to optimal policies and significant improvements over methods that assume i.i.d. demand.
I would like to thank the 2025 ICS Harvey J. Greenberg Research Award Committee for their diligent work:
Chairs
- Selva Nadarajah, University of Illinois Chicago
- Andre A. Cire, University of Toronto Scarborough & Rotman School of Management
Steering Committee
- Dan Adelman, University of Chicago
- David Brown, Duke University
- Ricardo Fukasawa, University of Waterloo
- Simge Küçükyavuz, Northwestern University
- Siqian Shen, University of Michigan at Ann Arbor
- Golbon Zakeri, University of Massachusetts Amherst
Review Panel
- Yi-Chun Akchen, University College London
- Margarida Carvalho, Université de Montréal
- Margarita Paz Castro, Pontificia Universidad Católica de Chile
- Levi DeValve, University of Chicago
- Ludwig Dierks, University of Illinois Chicago
- Daniel Jiang, Meta
- Carla Michini, University of Wisconsin-Madison
- Raghav Singal, Dartmouth
Please join us at the ICS business meeting during the INFORMS 2025 Annual Meeting in Atlanta on Monday, October 27, to recognize the contribution of our colleagues.
Sincerely,
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Thiago Serra
Assistant Professor of Business Analytics, University of Iowa
INFORMS Computing Society Chair (2024-2025)
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